Design Challenges for Machine/Deep Learning Algorithms

Dharmik, R. C.; Bawankar, Bhushan

doi:10.1002/9781119910497.ch13

Cited by 2 publications

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“…This has led to missing values in multi‐source data integration (garbage in, garbage out). The academic community has formed a consensus regarding the significant issues of similar data quality [29,30] . The issue of missing data serves as a common problem among multi‐source data, [31] as demonstrated in research fields such as medicine to reduce model performance [32,33] .…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

Gong,

Lei,

Sun

et al. 2024

Materials Genome Engineering Advances

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Accurately and quickly predicting hydrogen embrittlement performance is critical for the service of metal materials. However, due to multi‐source heterogeneity, existing hydrogen embrittlement data are missing, making it impractical to train reliable machine learning models. In this study, we proposed an ensemble learning training strategy for missing data based on the Adaboost algorithm. This method introduced a mask matrix with missing data and enabled each round of training to generate sub‐datasets, considering missing value information. The strategy first trained a subset of features based on the existing dataset and a selected method and continuously focused on the combination of features with the highest error for iterative training, where the mask matrix of the missing data was used as the input to fit the weights of each base learner using a neural network. Compared with directly modeling on highly sparse data, the predictive ability of this strategy was significantly improved by approximately 20%. In addition, in the testing of new samples, the predicted mean absolute error of the new model was successfully reduced from 0.2 to 0.09. This strategy offers good adaptability to the hydrogen embrittlement sensitivity of different sizes and can avoid interference from feature importance caused by filling data.

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Section: Introductionmentioning

confidence: 99%

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

Gong,

Lei,

Sun

et al. 2024

Materials Genome Engineering Advances

View full text Add to dashboard Cite

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Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

Dejene,

Lemu,

Gutema

2024

Int J Adv Manuf Technol

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Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

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Design Challenges for Machine/Deep Learning Algorithms

Cited by 2 publications

References 0 publications

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

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